COOP
Brian
7/27/2022
Disclaimer
This work is very preliminary as I get back into the coding swing of things. Data wrangling and figure generation will be done via R, but the rest of the project will be done using good ol’ microsoft products. This is just an entry point into data crunching and should by no means be considered a final product.
3/30/2023 update, corrected a minor error in methodology.
Steamboat COOP
Coop Data
NOAA/NWS Cooperative Observer Network
Two stations near steamboat, according to this site. 057936 02 SBTC2 UNITED STATES CO ROUTT +7 STEAMBOAT SPRINGS 40 30 17 -106 51 58 6636 057942 02 SSPC2 UNITED STATES CO ROUTT +7 STEAMBOAT SPRINGS 1 W 40 29 00 -106 51 00 6700
Downloading from the Iowa Environmental Mesonet. NOAA package not working, maybe due to user error.
Data Cleaning
#coop_steamboat_clean <- read.csv("C:/Users/bsteen/OneDrive - #Colostate/Documents/MS_Research/research-COOP/data_clean/coop_steamboat_clean.csv", header = TRUE)Figure check
# from home
coop_steamboat_clean <- read.csv("C:/Users/13074/Documents/ESS580/thesis_project/research-COOP/data_clean/coop_steamboat_clean.csv", header = TRUE)
coop_steamboat_clean$Date <- ymd(coop_steamboat_clean$Date)
str(coop_steamboat_clean)## 'data.frame': 44740 obs. of 13 variables:
## $ ï..station : chr "CO7936" "CO7936" "CO7936" "CO7936" ...
## $ station_name: chr "STEAMBOAT SPRINGS" "STEAMBOAT SPRINGS" "STEAMBOAT SPRINGS" "STEAMBOAT SPRINGS" ...
## $ lat : num 40.5 40.5 40.5 40.5 40.5 ...
## $ lon : num -107 -107 -107 -107 -107 ...
## $ day : chr "1/1/1900" "1/2/1900" "1/3/1900" "1/4/1900" ...
## $ doy : int 1 2 3 4 5 6 7 8 9 10 ...
## $ max_T_c : num -5.6 -0.6 5 7.8 3.3 4.4 -1.7 0.6 0.6 0.6 ...
## $ min_T_c : num -21.1 -17.8 -6.1 -5.6 -10.6 -9.4 -18.9 -15.6 -15 -18.9 ...
## $ avg_T_c : num -13.35 -9.2 -0.55 1.1 -3.65 ...
## $ Date : Date, format: "1900-01-01" "1900-01-02" ...
## $ waterYear : int 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 ...
## $ daymonth : chr "01-01" "02-01" "03-01" "04-01" ...
## $ waterDay : int 93 94 95 96 97 98 99 100 101 102 ...ggplot(coop_steamboat_clean, aes(x = Date, y = avg_T_c)) +
geom_line() + #lwd = 2) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('Daily temperature (°C)') +
xlab('Date')
#Check for outliers....
#dygraph
COOP_temp_xts <- xts(coop_steamboat_clean$avg_T_c, order.by = coop_steamboat_clean$Date)
dygraph(COOP_temp_xts) %>%
dyAxis("y", label = "Daily temperature (°C)") Detrending Data
#SF figured out the yearly average by water year
#average water year temperature
COOP_yearly_wy_aver <- coop_steamboat_clean %>%
group_by(waterYear) %>%
mutate(aver_ann_temp = mean(avg_T_c))#Average temperature by day for all water years:
COOP_daily_wy_aver <- COOP_yearly_wy_aver %>%
group_by(daymonth) %>%
mutate(aver_day_temp = mean(avg_T_c))
#average mean temperature by day for the period of record:
COOP_daily_wy_aver <- COOP_daily_wy_aver %>%
group_by(daymonth) %>%
mutate(all_ave_temp = mean(COOP_daily_wy_aver$aver_day_temp))
#str(daily_wy_aver)# try to show all years as means.
COOP_daily_wy_aver2 <- COOP_daily_wy_aver %>%
#filter(waterYear == "1987" | waterYear == "2021") %>%
group_by(waterDay) %>%
mutate(date_temp = mean(avg_T_c))
COOP_daily_wy_aver2$date_temp <- signif(COOP_daily_wy_aver2$date_temp,3) #reduce the sig figs
ggplot(COOP_daily_wy_aver2, aes(x = waterDay, y = date_temp))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
#geom_smooth(method = "lm", se=FALSE) +
ylab('Average Daily temperature (°C)') +
xlab('Day of water year')
Standard Deviation
To figure out the standard deviation for each year, I want the “residual” for each daily value.
The standard deviation will be the daily residual minus the mean of the residuals by water year, summed and squared, then divided by the number of observations minus one. The square root of the resulting value of which is thus the standard deviation for the water year.
Determining residuals
COOP_standard_dev <- COOP_daily_wy_aver %>%
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual)))
mean(COOP_standard_dev$residual)## [1] -9.652271e-17The mean of the residuals is close enough to zero
Calculating standard deviation for the timeseries
COOP_standard_dev_all <- COOP_standard_dev %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all <- COOP_standard_dev_all %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1900 | 3.387043 |
| 1901 | 4.068619 |
| 1902 | 3.568775 |
| 1903 | 3.527189 |
| 1904 | 3.696915 |
| 1905 | 3.402598 |
| 1906 | 3.167953 |
| 1907 | 3.996705 |
| 1908 | 3.453319 |
| 1909 | 4.792216 |
| 1910 | 4.499426 |
| 1911 | 4.143812 |
| 1912 | 3.935198 |
| 1913 | 4.109351 |
| 1914 | 4.003080 |
| 1915 | 3.687804 |
| 1916 | 3.984756 |
| 1917 | 4.499360 |
| 1918 | 3.987734 |
| 1919 | 3.751334 |
| 1920 | 4.167794 |
| 1921 | 4.301257 |
| 1922 | 4.401713 |
| 1923 | 3.695604 |
| 1924 | 3.607483 |
| 1925 | 3.947664 |
| 1926 | 3.400954 |
| 1927 | 4.236689 |
| 1928 | 4.342036 |
| 1929 | 4.295103 |
| 1930 | 4.208991 |
| 1931 | 3.797143 |
| 1932 | 4.291793 |
| 1933 | 4.295393 |
| 1934 | 3.508106 |
| 1935 | 3.647796 |
| 1936 | 3.725678 |
| 1937 | 3.945438 |
| 1938 | 3.655086 |
| 1939 | 4.076417 |
| 1940 | 3.720026 |
| 1941 | 3.833928 |
| 1942 | 4.269962 |
| 1943 | 4.039773 |
| 1944 | 3.637570 |
| 1945 | 3.828605 |
| 1946 | 3.936963 |
| 1947 | 3.833509 |
| 1948 | 4.127381 |
| 1949 | 3.747441 |
| 1950 | 4.092183 |
| 1951 | 4.305097 |
| 1952 | 4.137060 |
| 1953 | 4.104637 |
| 1954 | 3.493113 |
| 1955 | 4.223100 |
| 1956 | 4.473028 |
| 1957 | 4.227564 |
| 1958 | 3.727149 |
| 1959 | 3.861405 |
| 1960 | 3.846644 |
| 1961 | 3.447334 |
| 1962 | 4.549574 |
| 1963 | 4.254752 |
| 1964 | 4.116082 |
| 1965 | 4.272005 |
| 1966 | 3.505496 |
| 1967 | 3.952601 |
| 1968 | 3.786388 |
| 1969 | 4.124864 |
| 1970 | 4.031999 |
| 1971 | 4.382322 |
| 1972 | 4.300176 |
| 1973 | 4.211447 |
| 1974 | 3.657256 |
| 1975 | 4.026061 |
| 1976 | 3.425550 |
| 1977 | 3.371104 |
| 1978 | 3.745753 |
| 1979 | 4.228923 |
| 1980 | 3.970125 |
| 1981 | 3.800191 |
| 1982 | 4.062310 |
| 1983 | 3.823264 |
| 1984 | 3.937347 |
| 1985 | 4.034121 |
| 1986 | 3.904996 |
| 1987 | 3.411860 |
| 1988 | 3.964910 |
| 1989 | 4.381722 |
| 1990 | 3.725159 |
| 1991 | 4.245862 |
| 1992 | 4.030424 |
| 1993 | 4.086199 |
| 1994 | 3.757401 |
| 1995 | 4.037364 |
| 1996 | 3.935450 |
| 1997 | 4.092659 |
| 1998 | 3.733417 |
| 1999 | 4.194017 |
| 2000 | 3.619707 |
| 2001 | 4.252626 |
| 2002 | 4.292146 |
| 2003 | 3.756071 |
| 2004 | 3.940469 |
| 2005 | 3.678190 |
| 2006 | 4.100780 |
| 2007 | 4.276960 |
| 2008 | 3.721782 |
| 2009 | 3.681365 |
| 2010 | 3.902448 |
| 2011 | 4.177220 |
| 2012 | 3.455744 |
| 2013 | 4.447458 |
| 2014 | 3.878538 |
| 2015 | 4.109502 |
| 2016 | 3.583526 |
| 2017 | 4.422096 |
| 2018 | 3.662640 |
| 2019 | 4.031328 |
| 2020 | 4.427930 |
| 2021 | 3.851773 |
| 2022 | 4.567149 |
ggplot(COOP_standard_dev_all, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Update 5/4/2023
write.csv(COOP_standard_dev_all,"C:/Users/13074/Documents/ESS580/thesis_project/research-COOP/data_clean/COOP_standard_dev_all.csv", row.names = FALSE)# THIS IS A NIGHTMARE (but it worked).
auto_cor_SD <-COOP_standard_dev_all %>%
mutate(Decade = paste0(waterYear %/% 10 * 10))
acf_1900 <- auto_cor_SD %>%
filter(Decade == "1900")
acf_1910 <- auto_cor_SD %>%
filter(Decade == "1910")
acf_1920 <- auto_cor_SD %>%
filter(Decade == "1920")
acf_1930 <- auto_cor_SD %>%
filter(Decade == "1930")
acf_1940 <- auto_cor_SD %>%
filter(Decade == "1940")
acf_1950 <- auto_cor_SD %>%
filter(Decade == "1950")
acf_1960 <- auto_cor_SD %>%
filter(Decade == "1960")
acf_1970 <- auto_cor_SD %>%
filter(Decade == "1970")
acf_1980 <- auto_cor_SD %>%
filter(Decade == "1980")
acf_1990 <- auto_cor_SD %>%
filter(Decade == "1990")
acf_2000 <- auto_cor_SD %>%
filter(Decade == "2000")
acf_2010 <- auto_cor_SD %>%
filter(Decade == "2010" | Decade == "2020")
acf(acf_1900$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_1900$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 -0.239 0.202 -0.160 -0.207 -0.052acf(acf_1910$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_1910$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 0.037 -0.533 0.129 0.143 -0.320acf(acf_1920$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_1920$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 0.231 -0.047 -0.218 -0.470 -0.284acf(acf_1930$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_1930$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 0.018 -0.087 0.018 -0.113 -0.277acf(acf_1940$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_1940$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 -0.095 -0.486 -0.097 0.019 0.166acf(acf_1950$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_1950$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 0.076 -0.525 -0.442 0.286 0.283acf(acf_1960$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_1960$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 -0.135 -0.049 -0.187 -0.402 0.151acf(acf_1970$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_1970$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 0.414 0.088 -0.178 -0.206 -0.464acf(acf_1980$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_1980$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 -0.054 -0.526 -0.001 0.162 -0.116acf(acf_1990$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_1990$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 -0.537 0.190 -0.422 0.462 -0.293acf(acf_2000$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_2000$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 -0.083 -0.484 -0.073 -0.032 0.430acf(acf_2010$sd_2, lag=5, pl=FALSE)##
## Autocorrelations of series 'acf_2010$sd_2', by lag
##
## 0 1 2 3 4 5
## 1.000 -0.610 0.383 -0.113 0.043 -0.087#put the output into an excel file, reading it back into R. #failingbutwinning
steamboat_autocorrelation <- read_excel("data_clean/steamboat_autocorrelation.xlsx")
#View(steamboat_autocorrelation)
steamboat_autocorrelation <- steamboat_autocorrelation %>%
filter(Decade != "2010_2022")
ggplot(steamboat_autocorrelation, aes(x = Decade))+
geom_point(aes(y = lag_1, color = "lag_1"), size =7)+
geom_point(aes(y = lag_2, color = "lag_2"), size =5)+
geom_point(aes(y = lag_3, color = "lag_3"), size =4)+
theme_few()+
scale_colour_manual(name = "lags", values=c("lag_1"="red", "lag_2" = "purple", "lag_3" ="blue"))+#, "noaa_morrisey" = "red", "oyler" = "purple"))+#, "oyler_noaa_conus" = "grey"))+
xlab("Decade")+
ylab("Autocorrelation")
coop_steamboat_clean_annual_t <- coop_steamboat_clean %>%
group_by(waterYear) %>%
mutate(mean_temp_annual = mean(avg_T_c)) %>%
distinct(waterYear, .keep_all = TRUE)
ggplot(coop_steamboat_clean_annual_t, aes(x = waterYear, y = mean_temp_annual)) +
geom_point() + #lwd = 2) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('Mean annual temperature (°C)') +
xlab('Water Year')
Checking a random year from the timeseries.
COOP_standard_dev_87 <- COOP_standard_dev %>%
filter(waterYear == 1987) %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean)
COOP_standard_dev_87 <- COOP_standard_dev_87 %>%
group_by(waterYear) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((sum(tabulate(COOP_standard_dev_87$waterDay)))-1)))^(0.5))
head(COOP_standard_dev_87$sd_2, 1)## [1] 3.41186Looks good.
Summer temperature standard deviation
COOP_standard_dev_all_summer <- COOP_standard_dev %>%
filter(waterDay >= 244 & waterDay <= 335) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_summer <- COOP_standard_dev_all_summer %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_summer %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1900 | 2.343534 |
| 1901 | 2.052822 |
| 1902 | 2.370343 |
| 1903 | 2.038227 |
| 1904 | 1.629967 |
| 1905 | 1.889724 |
| 1906 | 1.768985 |
| 1907 | 1.296196 |
| 1908 | 1.748968 |
| 1909 | 2.170115 |
| 1910 | 2.000957 |
| 1911 | 2.208233 |
| 1912 | 2.288062 |
| 1913 | 2.526328 |
| 1914 | 1.713976 |
| 1915 | 2.080550 |
| 1916 | 2.122182 |
| 1917 | 1.921234 |
| 1918 | 2.714718 |
| 1919 | 2.404358 |
| 1920 | 2.379640 |
| 1921 | 2.272216 |
| 1922 | 1.883791 |
| 1923 | 1.584776 |
| 1924 | 2.169737 |
| 1925 | 2.168214 |
| 1926 | 2.394504 |
| 1927 | 2.673759 |
| 1928 | 2.450568 |
| 1929 | 2.395691 |
| 1930 | 1.470897 |
| 1931 | 2.339435 |
| 1932 | 2.157538 |
| 1933 | 1.947806 |
| 1934 | 1.711129 |
| 1935 | 1.957617 |
| 1936 | 2.086720 |
| 1937 | 2.119000 |
| 1938 | 1.939949 |
| 1939 | 2.255753 |
| 1940 | 2.115228 |
| 1941 | 1.983932 |
| 1942 | 1.681608 |
| 1943 | 2.104935 |
| 1944 | 1.844256 |
| 1945 | 2.463376 |
| 1946 | 1.699111 |
| 1947 | 2.293489 |
| 1948 | 2.131599 |
| 1949 | 1.329732 |
| 1950 | 1.680547 |
| 1951 | 2.123582 |
| 1952 | 1.787384 |
| 1953 | 2.011197 |
| 1954 | 2.419949 |
| 1955 | 2.080150 |
| 1956 | 1.824454 |
| 1957 | 2.189850 |
| 1958 | 1.994329 |
| 1959 | 2.140958 |
| 1960 | 1.620575 |
| 1961 | 1.606953 |
| 1962 | 2.063112 |
| 1963 | 1.526357 |
| 1964 | 2.460590 |
| 1965 | 1.479728 |
| 1966 | 1.769717 |
| 1967 | 1.429726 |
| 1968 | 2.292879 |
| 1969 | 2.257943 |
| 1970 | 1.902575 |
| 1971 | 1.787828 |
| 1972 | 1.928531 |
| 1973 | 2.586347 |
| 1974 | 2.239603 |
| 1975 | 1.953987 |
| 1976 | 2.229307 |
| 1977 | 1.959354 |
| 1978 | 1.810978 |
| 1979 | 1.875752 |
| 1980 | 1.908943 |
| 1981 | 2.143438 |
| 1982 | 2.335733 |
| 1983 | 2.053450 |
| 1984 | 1.967233 |
| 1985 | 2.174145 |
| 1986 | 1.863319 |
| 1987 | 1.904726 |
| 1988 | 1.986655 |
| 1989 | 2.285310 |
| 1990 | 2.617198 |
| 1991 | 1.793621 |
| 1992 | 2.324413 |
| 1993 | 2.552794 |
| 1994 | 2.116211 |
| 1995 | 2.749449 |
| 1996 | 1.750358 |
| 1997 | 2.340247 |
| 1998 | 2.430688 |
| 1999 | 1.866542 |
| 2000 | 2.215133 |
| 2001 | 2.618700 |
| 2002 | 2.528510 |
| 2003 | 2.415446 |
| 2004 | 2.541391 |
| 2005 | 2.222956 |
| 2006 | 2.165075 |
| 2007 | 2.405073 |
| 2008 | 2.596269 |
| 2009 | 1.977769 |
| 2010 | 2.115651 |
| 2011 | 1.943490 |
| 2012 | 2.072429 |
| 2013 | 1.943989 |
| 2014 | 2.253113 |
| 2015 | 2.337471 |
| 2016 | 1.947347 |
| 2017 | 1.914768 |
| 2018 | 2.316994 |
| 2019 | 2.483465 |
| 2020 | 2.575885 |
| 2021 | 2.482696 |
| 2022 | 3.147948 |
ggplot(COOP_standard_dev_all_summer, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Jun-Aug standard deviation for water years 1900-2021
Mann-Kendall & Sen’s Slope
Summer standard deviations.
sd_mk_summer <- mk.test(COOP_standard_dev_all_summer$sd_2)
print(sd_mk_summer)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_summer$sd_2
## z = 2.2211, n = 123, p-value = 0.02635
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## 1.017000e+03 2.092503e+05 1.355458e-01sd_sens_summer <- sens.slope(COOP_standard_dev_all_summer$sd_2)
print(sd_sens_summer)##
## Sen's slope
##
## data: COOP_standard_dev_all_summer$sd_2
## z = 2.2211, n = 123, p-value = 0.02635
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## 0.0002669607 0.0035814520
## sample estimates:
## Sen's slope
## 0.001931784Winter temperature standard deviation
COOP_standard_dev_all_winter <- COOP_standard_dev %>%
filter(waterDay >= 32 & waterDay <= 182) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(nmbr = n())
# Nope. This did some weird stuff with twice the observations.
# COOP_standard_dev_all_winter <- COOP_standard_dev %>%
# filter(daymonth >= "01-11" & daymonth <= "31-03") %>%
# group_by(waterYear) %>%
# mutate(nmbr = n())
COOP_standard_dev_all_winter <- COOP_standard_dev_all_winter %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_winter %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1900 | 4.032437 |
| 1901 | 5.152863 |
| 1902 | 4.580459 |
| 1903 | 4.296317 |
| 1904 | 4.277880 |
| 1905 | 4.516199 |
| 1906 | 4.042376 |
| 1907 | 3.615871 |
| 1908 | 4.196627 |
| 1909 | 6.027292 |
| 1910 | 6.176796 |
| 1911 | 5.179486 |
| 1912 | 5.285784 |
| 1913 | 5.418424 |
| 1914 | 5.124939 |
| 1915 | 4.492648 |
| 1916 | 5.320355 |
| 1917 | 5.960599 |
| 1918 | 5.006565 |
| 1919 | 4.710630 |
| 1920 | 5.211839 |
| 1921 | 5.465862 |
| 1922 | 6.082555 |
| 1923 | 4.710294 |
| 1924 | 4.409977 |
| 1925 | 4.966388 |
| 1926 | 3.926105 |
| 1927 | 5.161938 |
| 1928 | 5.548213 |
| 1929 | 5.336150 |
| 1930 | 5.268585 |
| 1931 | 4.785408 |
| 1932 | 5.872585 |
| 1933 | 5.506158 |
| 1934 | 4.296671 |
| 1935 | 4.823304 |
| 1936 | 4.478443 |
| 1937 | 5.261662 |
| 1938 | 4.633655 |
| 1939 | 5.180047 |
| 1940 | 4.921106 |
| 1941 | 5.032277 |
| 1942 | 5.812571 |
| 1943 | 5.213421 |
| 1944 | 4.709201 |
| 1945 | 4.595392 |
| 1946 | 5.257146 |
| 1947 | 4.751165 |
| 1948 | 5.401913 |
| 1949 | 5.149603 |
| 1950 | 5.149961 |
| 1951 | 5.986132 |
| 1952 | 5.734292 |
| 1953 | 5.455903 |
| 1954 | 4.601240 |
| 1955 | 5.585458 |
| 1956 | 6.282696 |
| 1957 | 5.781879 |
| 1958 | 5.015481 |
| 1959 | 4.849098 |
| 1960 | 5.105714 |
| 1961 | 4.087986 |
| 1962 | 6.156861 |
| 1963 | 6.117983 |
| 1964 | 5.350773 |
| 1965 | 5.941975 |
| 1966 | 4.716248 |
| 1967 | 5.124600 |
| 1968 | 4.841351 |
| 1969 | 5.489272 |
| 1970 | 5.297339 |
| 1971 | 5.753360 |
| 1972 | 6.016482 |
| 1973 | 5.445182 |
| 1974 | 4.815183 |
| 1975 | 5.371131 |
| 1976 | 4.575222 |
| 1977 | 4.206960 |
| 1978 | 4.424031 |
| 1979 | 5.922070 |
| 1980 | 5.425155 |
| 1981 | 4.268802 |
| 1982 | 5.399768 |
| 1983 | 4.560962 |
| 1984 | 5.047083 |
| 1985 | 5.158547 |
| 1986 | 5.208834 |
| 1987 | 4.194948 |
| 1988 | 5.013068 |
| 1989 | 5.757657 |
| 1990 | 4.458001 |
| 1991 | 5.520284 |
| 1992 | 4.848221 |
| 1993 | 5.208729 |
| 1994 | 4.739457 |
| 1995 | 5.039713 |
| 1996 | 4.769257 |
| 1997 | 4.751369 |
| 1998 | 4.682027 |
| 1999 | 5.320874 |
| 2000 | 4.075872 |
| 2001 | 4.969522 |
| 2002 | 5.145239 |
| 2003 | 4.611630 |
| 2004 | 4.854507 |
| 2005 | 4.674537 |
| 2006 | 5.038415 |
| 2007 | 5.375765 |
| 2008 | 4.262245 |
| 2009 | 4.469792 |
| 2010 | 4.613645 |
| 2011 | 5.463104 |
| 2012 | 4.168804 |
| 2013 | 5.434713 |
| 2014 | 4.645119 |
| 2015 | 5.265263 |
| 2016 | 4.279881 |
| 2017 | 5.543005 |
| 2018 | 4.391600 |
| 2019 | 4.616723 |
| 2020 | 4.637958 |
| 2021 | 4.022003 |
| 2022 | 5.033060 |
ggplot(COOP_standard_dev_all_winter, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Nov-Mar standard deviation for water years 1900-2021
Mann-Kendall & Sen’s Slope
Winter standard deviations.
sd_mk_winter <- mk.test(COOP_standard_dev_all_winter$sd_2)
print(sd_mk_winter)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_winter$sd_2
## z = -0.99685, n = 123, p-value = 0.3188
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## -4.570000e+02 2.092503e+05 -6.090897e-02sd_sens_winter <- sens.slope(COOP_standard_dev_all_winter$sd_2)
print(sd_sens_winter)##
## Sen's slope
##
## data: COOP_standard_dev_all_winter$sd_2
## z = -0.99685, n = 123, p-value = 0.3188
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.004753212 0.001474537
## sample estimates:
## Sen's slope
## -0.001523523Smaller time increments might be useful….
1987 - 2021
In comparison to the Tower SNOTEL site:
# I did this wrong- need to compare the 1987- 2021 time series without the mean of the 1900 - 2021 time series influencing the residuals.
#average water year temperature
COOP_yearly_wy_aver_87_21 <- coop_steamboat_clean %>%
filter(waterYear >= 1987 & waterYear <= 2021) %>%
group_by(waterYear) %>%
mutate(aver_ann_temp = mean(avg_T_c))
#Average temperature by day for all water years:
COOP_daily_wy_aver_87_21 <- COOP_yearly_wy_aver_87_21 %>%
group_by(daymonth) %>%
mutate(aver_day_temp = mean(avg_T_c))
#average mean temperature by day for the period of record:
COOP_daily_wy_aver_87_21 <- COOP_daily_wy_aver_87_21 %>%
group_by(daymonth) %>%
mutate(all_ave_temp = mean(COOP_daily_wy_aver_87_21$aver_day_temp)) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_87_21 <- COOP_daily_wy_aver_87_21 %>%
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual)))
COOP_standard_dev_87_21 <- COOP_standard_dev_87_21 %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_87_21 %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1987 | 3.424576 |
| 1988 | 3.931157 |
| 1989 | 4.301842 |
| 1990 | 3.583093 |
| 1991 | 4.176727 |
| 1992 | 3.937969 |
| 1993 | 3.973356 |
| 1994 | 3.691415 |
| 1995 | 3.964424 |
| 1996 | 3.939454 |
| 1997 | 4.033360 |
| 1998 | 3.667026 |
| 1999 | 4.157142 |
| 2000 | 3.663586 |
| 2001 | 4.116828 |
| 2002 | 4.183094 |
| 2003 | 3.693382 |
| 2004 | 3.871310 |
| 2005 | 3.644129 |
| 2006 | 4.118883 |
| 2007 | 4.051204 |
| 2008 | 3.733130 |
| 2009 | 3.738246 |
| 2010 | 3.926954 |
| 2011 | 4.218237 |
| 2012 | 3.297969 |
| 2013 | 4.352540 |
| 2014 | 3.875402 |
| 2015 | 4.077057 |
| 2016 | 3.566496 |
| 2017 | 4.354637 |
| 2018 | 3.658564 |
| 2019 | 3.989471 |
| 2020 | 4.365474 |
| 2021 | 3.782126 |
ggplot(COOP_standard_dev_87_21, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
SD for water years 1987 - 2021
1987 - 2021 Mann-Kendall & Sen’s Slope
sd_mk_87_21 <- mk.test(COOP_standard_dev_87_21$sd_2)
print(sd_mk_87_21)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_87_21$sd_2
## z = 0.68167, n = 35, p-value = 0.4954
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## 4.900000e+01 4.958333e+03 8.235294e-02sd_sens_87_21 <- sens.slope(COOP_standard_dev_87_21$sd_2)
print(sd_sens_87_21)##
## Sen's slope
##
## data: COOP_standard_dev_87_21$sd_2
## z = 0.68167, n = 35, p-value = 0.4954
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.007011434 0.013956979
## sample estimates:
## Sen's slope
## 0.003231668Summer and Winter 87-21
Summer temperature standard deviation
# using the 1987- 2021 data frame
COOP_standard_dev_all_87_21_summer <- COOP_daily_wy_aver_87_21 %>%
filter(waterDay >= 244 & waterDay <= 335) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual))) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_87_21_summer <- COOP_standard_dev_all_87_21_summer %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_87_21_summer %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1987 | 1.935143 |
| 1988 | 1.993794 |
| 1989 | 2.230940 |
| 1990 | 2.544550 |
| 1991 | 1.775141 |
| 1992 | 2.332864 |
| 1993 | 2.568171 |
| 1994 | 2.178839 |
| 1995 | 2.786218 |
| 1996 | 1.789394 |
| 1997 | 2.339336 |
| 1998 | 2.382172 |
| 1999 | 1.828553 |
| 2000 | 2.201437 |
| 2001 | 2.627660 |
| 2002 | 2.511709 |
| 2003 | 2.405932 |
| 2004 | 2.511997 |
| 2005 | 2.219459 |
| 2006 | 2.134931 |
| 2007 | 2.358026 |
| 2008 | 2.534519 |
| 2009 | 1.916377 |
| 2010 | 2.109093 |
| 2011 | 1.851202 |
| 2012 | 1.991655 |
| 2013 | 1.970984 |
| 2014 | 2.214680 |
| 2015 | 2.302278 |
| 2016 | 1.998966 |
| 2017 | 1.877157 |
| 2018 | 2.294063 |
| 2019 | 2.407073 |
| 2020 | 2.540686 |
| 2021 | 2.469644 |
ggplot(COOP_standard_dev_all_87_21_summer, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Summer season SD for water years 1987 - 2021
Mann-Kendall & Sen’s Slope
Summer 87-21 standard deviations.
sd_mk_summer <- mk.test(COOP_standard_dev_all_87_21_summer$sd_2)
print(sd_mk_summer)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_87_21_summer$sd_2
## z = 0.17042, n = 35, p-value = 0.8647
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## 1.300000e+01 4.958333e+03 2.184874e-02sd_sens_summer <- sens.slope(COOP_standard_dev_all_summer$sd_2)
print(sd_sens_summer)##
## Sen's slope
##
## data: COOP_standard_dev_all_summer$sd_2
## z = 2.2211, n = 123, p-value = 0.02635
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## 0.0002669607 0.0035814520
## sample estimates:
## Sen's slope
## 0.001931784Winter temperature standard deviation
# using the 1987- 2021 data frame
COOP_standard_dev_all_87_21_winter <- COOP_daily_wy_aver_87_21 %>%
filter(waterDay >= 32 & waterDay <= 182) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual))) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_87_21_winter <- COOP_standard_dev_all_87_21_winter %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_87_21_winter %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1987 | 4.169279 |
| 1988 | 4.979472 |
| 1989 | 5.615506 |
| 1990 | 4.256383 |
| 1991 | 5.449026 |
| 1992 | 4.557310 |
| 1993 | 4.986382 |
| 1994 | 4.662775 |
| 1995 | 4.923980 |
| 1996 | 4.826996 |
| 1997 | 4.671113 |
| 1998 | 4.633265 |
| 1999 | 5.189225 |
| 2000 | 4.159790 |
| 2001 | 4.845489 |
| 2002 | 5.107031 |
| 2003 | 4.524114 |
| 2004 | 4.696476 |
| 2005 | 4.530915 |
| 2006 | 5.149943 |
| 2007 | 5.022096 |
| 2008 | 4.291589 |
| 2009 | 4.508637 |
| 2010 | 4.696043 |
| 2011 | 5.614273 |
| 2012 | 3.965244 |
| 2013 | 5.404382 |
| 2014 | 4.665509 |
| 2015 | 5.102774 |
| 2016 | 4.276957 |
| 2017 | 5.371856 |
| 2018 | 4.382774 |
| 2019 | 4.707970 |
| 2020 | 4.582198 |
| 2021 | 4.031273 |
ggplot(COOP_standard_dev_all_87_21_winter, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Winter season SD for water years 1987 - 2021
Mann-Kendall & Sen’s Slope
1987 - 2021 winter Mann-Kendall & Sen’s Slope
sd_mk_87_21_winter <- mk.test(COOP_standard_dev_all_87_21_winter$sd_2)
print(sd_mk_87_21_winter)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_87_21_winter$sd_2
## z = -0.82368, n = 35, p-value = 0.4101
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## -59.00000000 4958.33333333 -0.09915966sd_sens_87_21_winter <- sens.slope(COOP_standard_dev_all_87_21_winter$sd_2)
print(sd_sens_87_21_winter)##
## Sen's slope
##
## data: COOP_standard_dev_all_87_21_winter$sd_2
## z = -0.82368, n = 35, p-value = 0.4101
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.024144341 0.009118474
## sample estimates:
## Sen's slope
## -0.007927524Neither the 1987 - 2021 as a whole, nor the seasonal time series demonstrate trends that are statistically significant.
1900-1915 minimum and maximum temperatures
Jan 7, 1913 had the lowest recorded temperature. This just looks at the max and min temperatures for that early part of the record.
early_coop <- coop_steamboat_clean %>%
filter(waterYear >= 1900 & waterYear <= 1915)
COOP_temp_min_xts <- xts(early_coop$min_T_c, order.by = early_coop$Date)
dygraph(COOP_temp_min_xts) %>%
dyAxis("y", label = "Daily min temperature (°C)") Daily minimum temperatures for water years 1900-1915
COOP_temp_max_xts <- xts(early_coop$max_T_c, order.by = early_coop$Date)
dygraph(COOP_temp_max_xts) %>%
dyAxis("y", label = "Daily max temperature (°C)") Daily minimum temperatures for water years 1900-1915
By groups of decades
1900-1930
#average water year temperature
COOP_yearly_wy_aver_00_30 <- coop_steamboat_clean %>%
filter(waterYear >= 1900 & waterYear <= 1930) %>%
group_by(waterYear) %>%
mutate(aver_ann_temp = mean(avg_T_c))
#Average temperature by day for all water years:
COOP_daily_wy_aver_00_30 <- COOP_yearly_wy_aver_00_30 %>%
group_by(daymonth) %>%
mutate(aver_day_temp = mean(avg_T_c))
#average mean temperature by day for the period of record:
COOP_daily_wy_aver_00_30 <- COOP_daily_wy_aver_00_30 %>%
group_by(daymonth) %>%
mutate(all_ave_temp = mean(COOP_daily_wy_aver_00_30$aver_day_temp)) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_00_30 <- COOP_daily_wy_aver_00_30 %>%
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual)))
COOP_standard_dev_00_30 <- COOP_standard_dev_00_30 %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_00_30 %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1900 | 3.201760 |
| 1901 | 3.990374 |
| 1902 | 3.495081 |
| 1903 | 3.498700 |
| 1904 | 3.566867 |
| 1905 | 3.350260 |
| 1906 | 3.103802 |
| 1907 | 3.966733 |
| 1908 | 3.464956 |
| 1909 | 4.752127 |
| 1910 | 4.430479 |
| 1911 | 4.027024 |
| 1912 | 4.005247 |
| 1913 | 4.093351 |
| 1914 | 3.911415 |
| 1915 | 3.677228 |
| 1916 | 3.900792 |
| 1917 | 4.528786 |
| 1918 | 3.859437 |
| 1919 | 3.921495 |
| 1920 | 4.094066 |
| 1921 | 4.162064 |
| 1922 | 4.342919 |
| 1923 | 3.735643 |
| 1924 | 3.683247 |
| 1925 | 3.807423 |
| 1926 | 3.383065 |
| 1927 | 3.993153 |
| 1928 | 4.145520 |
| 1929 | 4.180380 |
| 1930 | 4.206931 |
ggplot(COOP_standard_dev_00_30, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
SD for water years 1900 - 1930
1900 - 1930 Mann-Kendall & Sen’s Slope
sd_mk_00_30 <- mk.test(COOP_standard_dev_00_30$sd_2)
print(sd_mk_00_30)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_00_30$sd_2
## z = 2.1415, n = 31, p-value = 0.03223
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## 127.0000000 3461.6666667 0.2731183sd_sens_00_30 <- sens.slope(COOP_standard_dev_00_30$sd_2)
print(sd_sens_00_30)##
## Sen's slope
##
## data: COOP_standard_dev_00_30$sd_2
## z = 2.1415, n = 31, p-value = 0.03223
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## 0.001562166 0.032696841
## sample estimates:
## Sen's slope
## 0.01672178Summer and Winter 00-30
Summer temperature standard deviation
# using the 1900- 1930 data frame
COOP_standard_dev_all_00_30_summer <- COOP_daily_wy_aver_00_30 %>%
filter(waterDay >= 244 & waterDay <= 335) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual))) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_00_30_summer <- COOP_standard_dev_all_00_30_summer %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_00_30_summer %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1900 | 2.263739 |
| 1901 | 2.090944 |
| 1902 | 2.264615 |
| 1903 | 2.122756 |
| 1904 | 1.608957 |
| 1905 | 1.891343 |
| 1906 | 1.751173 |
| 1907 | 1.447252 |
| 1908 | 1.762553 |
| 1909 | 2.266312 |
| 1910 | 2.006374 |
| 1911 | 2.164735 |
| 1912 | 2.296291 |
| 1913 | 2.560634 |
| 1914 | 1.738455 |
| 1915 | 2.069813 |
| 1916 | 2.063861 |
| 1917 | 1.898723 |
| 1918 | 2.568409 |
| 1919 | 2.358741 |
| 1920 | 2.317614 |
| 1921 | 2.145945 |
| 1922 | 1.821744 |
| 1923 | 1.557476 |
| 1924 | 2.135836 |
| 1925 | 2.059975 |
| 1926 | 2.348773 |
| 1927 | 2.590941 |
| 1928 | 2.453216 |
| 1929 | 2.387445 |
| 1930 | 1.490211 |
ggplot(COOP_standard_dev_all_00_30_summer, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Summer season SD for water years 1900 - 1930
Mann-Kendall & Sen’s Slope
Summer 00-30 standard deviations.
sd_mk_00_30_summer <- mk.test(COOP_standard_dev_all_00_30_summer$sd_2)
print(sd_mk_00_30_summer)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_00_30_summer$sd_2
## z = 1.3257, n = 31, p-value = 0.1849
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## 79.0000000 3461.6666667 0.1698925sd_sens_00_30_summer <- sens.slope(COOP_standard_dev_all_00_30_summer$sd_2)
print(sd_sens_00_30_summer)##
## Sen's slope
##
## data: COOP_standard_dev_all_00_30_summer$sd_2
## z = 1.3257, n = 31, p-value = 0.1849
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.005609245 0.021399935
## sample estimates:
## Sen's slope
## 0.007379067Winter temperature standard deviation
# using the 00-30 data frame
COOP_standard_dev_all_00_30_winter <- COOP_daily_wy_aver_00_30 %>%
filter(waterDay >= 32 & waterDay <= 182) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual))) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_00_30_winter <- COOP_standard_dev_all_00_30_winter %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_00_30_winter %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1900 | 3.723162 |
| 1901 | 5.065432 |
| 1902 | 4.492115 |
| 1903 | 4.195997 |
| 1904 | 4.199892 |
| 1905 | 4.449922 |
| 1906 | 3.941312 |
| 1907 | 3.778798 |
| 1908 | 4.266280 |
| 1909 | 5.919534 |
| 1910 | 6.051690 |
| 1911 | 5.107299 |
| 1912 | 5.454531 |
| 1913 | 5.346476 |
| 1914 | 4.990005 |
| 1915 | 4.512612 |
| 1916 | 5.220994 |
| 1917 | 5.903666 |
| 1918 | 4.886774 |
| 1919 | 4.984953 |
| 1920 | 5.066982 |
| 1921 | 5.216569 |
| 1922 | 5.999659 |
| 1923 | 4.691835 |
| 1924 | 4.563404 |
| 1925 | 4.706124 |
| 1926 | 3.993937 |
| 1927 | 4.787616 |
| 1928 | 5.195486 |
| 1929 | 5.033618 |
| 1930 | 5.213335 |
ggplot(COOP_standard_dev_all_00_30_winter, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Winter season SD for water years 1900 - 1930
Mann-Kendall & Sen’s Slope
1900 - 1930 winter Mann-Kendall & Sen’s Slope
sd_mk_00_30_winter <- mk.test(COOP_standard_dev_all_00_30_winter$sd_2)
print(sd_mk_00_30_winter)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_00_30_winter$sd_2
## z = 1.5297, n = 31, p-value = 0.1261
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## 91.0000000 3461.6666667 0.1956989sd_sens_00_30_winter <- sens.slope(COOP_standard_dev_all_00_30_winter$sd_2)
print(sd_sens_00_30_winter)##
## Sen's slope
##
## data: COOP_standard_dev_all_00_30_winter$sd_2
## z = 1.5297, n = 31, p-value = 0.1261
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.00447963 0.04646028
## sample estimates:
## Sen's slope
## 0.02240291931-1960
#average water year temperature
COOP_yearly_wy_aver_31_60 <- coop_steamboat_clean %>%
filter(waterYear >= 1931 & waterYear <= 1960) %>%
group_by(waterYear) %>%
mutate(aver_ann_temp = mean(avg_T_c))
#Average temperature by day for all water years:
COOP_daily_wy_aver_31_60 <- COOP_yearly_wy_aver_31_60 %>%
group_by(daymonth) %>%
mutate(aver_day_temp = mean(avg_T_c))
#average mean temperature by day for the period of record:
COOP_daily_wy_aver_31_60 <- COOP_daily_wy_aver_31_60 %>%
group_by(daymonth) %>%
mutate(all_ave_temp = mean(COOP_daily_wy_aver_31_60$aver_day_temp)) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_31_60 <- COOP_daily_wy_aver_31_60 %>%
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual)))
COOP_standard_dev_31_60 <- COOP_standard_dev_31_60 %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_31_60 %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1931 | 3.760890 |
| 1932 | 4.193367 |
| 1933 | 4.319782 |
| 1934 | 3.529914 |
| 1935 | 3.567877 |
| 1936 | 3.702520 |
| 1937 | 3.878129 |
| 1938 | 3.610735 |
| 1939 | 3.971156 |
| 1940 | 3.672531 |
| 1941 | 3.800795 |
| 1942 | 4.099793 |
| 1943 | 3.912735 |
| 1944 | 3.570191 |
| 1945 | 3.735226 |
| 1946 | 3.906807 |
| 1947 | 3.798591 |
| 1948 | 4.062537 |
| 1949 | 3.668341 |
| 1950 | 4.116322 |
| 1951 | 4.225246 |
| 1952 | 4.056279 |
| 1953 | 4.107455 |
| 1954 | 3.577426 |
| 1955 | 4.177570 |
| 1956 | 4.385685 |
| 1957 | 4.170128 |
| 1958 | 3.652907 |
| 1959 | 3.800659 |
| 1960 | 3.774516 |
ggplot(COOP_standard_dev_31_60, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
SD for water years 1931 - 1960
1931 - 1960 Mann-Kendall & Sen’s Slope
sd_mk_31_60 <- mk.test(COOP_standard_dev_31_60$sd_2)
print(sd_mk_31_60)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_31_60$sd_2
## z = 1.3202, n = 30, p-value = 0.1868
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## 75.0000000 3141.6666667 0.1724138sd_sens_31_60 <- sens.slope(COOP_standard_dev_31_60$sd_2)
print(sd_sens_31_60)##
## Sen's slope
##
## data: COOP_standard_dev_31_60$sd_2
## z = 1.3202, n = 30, p-value = 0.1868
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.003720908 0.021202461
## sample estimates:
## Sen's slope
## 0.008983505Summer and Winter 31-60
Summer temperature standard deviation
# using the 1931- 1960 data frame
COOP_standard_dev_all_31_60_summer <- COOP_daily_wy_aver_31_60 %>%
filter(waterDay >= 244 & waterDay <= 335) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual))) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_31_60_summer <- COOP_standard_dev_all_31_60_summer %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_31_60_summer %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1931 | 2.233385 |
| 1932 | 2.110648 |
| 1933 | 1.930662 |
| 1934 | 1.641400 |
| 1935 | 1.888024 |
| 1936 | 2.023637 |
| 1937 | 2.102115 |
| 1938 | 1.945271 |
| 1939 | 2.301620 |
| 1940 | 2.008671 |
| 1941 | 1.896990 |
| 1942 | 1.696607 |
| 1943 | 2.023789 |
| 1944 | 1.894943 |
| 1945 | 2.383864 |
| 1946 | 1.708431 |
| 1947 | 2.286007 |
| 1948 | 2.194445 |
| 1949 | 1.312206 |
| 1950 | 1.738952 |
| 1951 | 1.999415 |
| 1952 | 1.768064 |
| 1953 | 1.950363 |
| 1954 | 2.430058 |
| 1955 | 1.958513 |
| 1956 | 1.838985 |
| 1957 | 2.258270 |
| 1958 | 2.032796 |
| 1959 | 2.024376 |
| 1960 | 1.568366 |
ggplot(COOP_standard_dev_all_31_60_summer, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Summer season SD for water years 1931 - 1960
Mann-Kendall & Sen’s Slope
Summer 31-60 standard deviations.
sd_mk_31_60_summer <- mk.test(COOP_standard_dev_all_31_60_summer$sd_2)
print(sd_mk_31_60_summer)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_31_60_summer$sd_2
## z = -0.24977, n = 30, p-value = 0.8028
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## -15.00000000 3141.66666667 -0.03448276sd_sens_31_60_summer <- sens.slope(COOP_standard_dev_all_31_60_summer$sd_2)
print(sd_sens_31_60_summer)##
## Sen's slope
##
## data: COOP_standard_dev_all_31_60_summer$sd_2
## z = -0.24977, n = 30, p-value = 0.8028
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.014148582 0.007630278
## sample estimates:
## Sen's slope
## -0.002408317Winter temperature standard deviation
# using the 31-60 data frame
COOP_standard_dev_all_31_60_winter <- COOP_daily_wy_aver_31_60 %>%
filter(waterDay >= 32 & waterDay <= 182) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual))) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_31_60_winter <- COOP_standard_dev_all_31_60_winter %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_31_60_winter %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1931 | 4.751342 |
| 1932 | 5.790312 |
| 1933 | 5.562620 |
| 1934 | 4.284776 |
| 1935 | 4.748514 |
| 1936 | 4.474569 |
| 1937 | 5.162471 |
| 1938 | 4.573951 |
| 1939 | 5.084584 |
| 1940 | 4.793605 |
| 1941 | 4.987704 |
| 1942 | 5.525195 |
| 1943 | 5.081059 |
| 1944 | 4.556901 |
| 1945 | 4.504257 |
| 1946 | 5.245315 |
| 1947 | 4.679191 |
| 1948 | 5.324723 |
| 1949 | 5.087298 |
| 1950 | 5.122150 |
| 1951 | 5.929128 |
| 1952 | 5.695226 |
| 1953 | 5.489698 |
| 1954 | 4.703118 |
| 1955 | 5.587329 |
| 1956 | 6.162403 |
| 1957 | 5.712359 |
| 1958 | 4.909039 |
| 1959 | 4.761019 |
| 1960 | 4.948515 |
ggplot(COOP_standard_dev_all_31_60_winter, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Winter season SD for water years 1931 - 1960
Mann-Kendall & Sen’s Slope
1931 - 1960 winter Mann-Kendall & Sen’s Slope
sd_mk_31_60_winter <- mk.test(COOP_standard_dev_all_31_60_winter$sd_2)
print(sd_mk_31_60_winter)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_31_60_winter$sd_2
## z = 1.6414, n = 30, p-value = 0.1007
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## 93.0000000 3141.6666667 0.2137931sd_sens_31_60_winter <- sens.slope(COOP_standard_dev_all_31_60_winter$sd_2)
print(sd_sens_31_60_winter)##
## Sen's slope
##
## data: COOP_standard_dev_all_31_60_winter$sd_2
## z = 1.6414, n = 30, p-value = 0.1007
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.003118133 0.042830343
## sample estimates:
## Sen's slope
## 0.01973811961-1990
#average water year temperature
COOP_yearly_wy_aver_61_90 <- coop_steamboat_clean %>%
filter(waterYear >= 1961 & waterYear <= 1990) %>%
group_by(waterYear) %>%
mutate(aver_ann_temp = mean(avg_T_c))
#Average temperature by day for all water years:
COOP_daily_wy_aver_61_90 <- COOP_yearly_wy_aver_61_90 %>%
group_by(daymonth) %>%
mutate(aver_day_temp = mean(avg_T_c))
#average mean temperature by day for the period of record:
COOP_daily_wy_aver_61_90 <- COOP_daily_wy_aver_61_90 %>%
group_by(daymonth) %>%
mutate(all_ave_temp = mean(COOP_daily_wy_aver_61_90$aver_day_temp)) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_61_90 <- COOP_daily_wy_aver_61_90 %>%
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual)))
COOP_standard_dev_61_90 <- COOP_standard_dev_61_90 %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_61_90 %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1961 | 3.356405 |
| 1962 | 4.532893 |
| 1963 | 4.290470 |
| 1964 | 4.043002 |
| 1965 | 4.287876 |
| 1966 | 3.519792 |
| 1967 | 3.881385 |
| 1968 | 3.715950 |
| 1969 | 4.106720 |
| 1970 | 3.940231 |
| 1971 | 4.283287 |
| 1972 | 4.232475 |
| 1973 | 4.157176 |
| 1974 | 3.552421 |
| 1975 | 3.917275 |
| 1976 | 3.416435 |
| 1977 | 3.309884 |
| 1978 | 3.721035 |
| 1979 | 4.065413 |
| 1980 | 3.968734 |
| 1981 | 3.755229 |
| 1982 | 3.899905 |
| 1983 | 3.798763 |
| 1984 | 3.970423 |
| 1985 | 3.972664 |
| 1986 | 3.782944 |
| 1987 | 3.502178 |
| 1988 | 3.887587 |
| 1989 | 4.262324 |
| 1990 | 3.756356 |
ggplot(COOP_standard_dev_61_90, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
SD for water years 1961 - 1990
1961 - 1990 Mann-Kendall & Sen’s Slope
sd_mk_61_90 <- mk.test(COOP_standard_dev_61_90$sd_2)
print(sd_mk_61_90)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_61_90$sd_2
## z = -1.1775, n = 30, p-value = 0.239
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## -67.000000 3141.666667 -0.154023sd_sens_61_90 <- sens.slope(COOP_standard_dev_61_90$sd_2)
print(sd_sens_61_90)##
## Sen's slope
##
## data: COOP_standard_dev_61_90$sd_2
## z = -1.1775, n = 30, p-value = 0.239
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.022821843 0.005520893
## sample estimates:
## Sen's slope
## -0.009830486Summer and Winter 61-90
Summer temperature standard deviation
# using the 1961- 1990 data frame
COOP_standard_dev_all_61_90_summer <- COOP_daily_wy_aver_61_90 %>%
filter(waterDay >= 244 & waterDay <= 335) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual))) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_61_90_summer <- COOP_standard_dev_all_61_90_summer %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_61_90_summer %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1961 | 1.603809 |
| 1962 | 2.004051 |
| 1963 | 1.463219 |
| 1964 | 2.423854 |
| 1965 | 1.530696 |
| 1966 | 1.759707 |
| 1967 | 1.376443 |
| 1968 | 2.256148 |
| 1969 | 2.240113 |
| 1970 | 1.813610 |
| 1971 | 1.826700 |
| 1972 | 1.927688 |
| 1973 | 2.561721 |
| 1974 | 2.295467 |
| 1975 | 1.892478 |
| 1976 | 2.192216 |
| 1977 | 1.940471 |
| 1978 | 1.799323 |
| 1979 | 1.858812 |
| 1980 | 1.875592 |
| 1981 | 2.094430 |
| 1982 | 2.355018 |
| 1983 | 1.999217 |
| 1984 | 1.991654 |
| 1985 | 2.149504 |
| 1986 | 1.868181 |
| 1987 | 1.954196 |
| 1988 | 1.986891 |
| 1989 | 2.275018 |
| 1990 | 2.559757 |
ggplot(COOP_standard_dev_all_61_90_summer, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Summer season SD for water years 1961 - 1990
Mann-Kendall & Sen’s Slope
Summer 61-90 standard deviations.
sd_mk_61_90_summer <- mk.test(COOP_standard_dev_all_61_90_summer$sd_2)
print(sd_mk_61_90_summer)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_61_90_summer$sd_2
## z = 2.0339, n = 30, p-value = 0.04196
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## 115.0000000 3141.6666667 0.2643678sd_sens_61_90_summer <- sens.slope(COOP_standard_dev_all_61_90_summer$sd_2)
print(sd_sens_61_90_summer)##
## Sen's slope
##
## data: COOP_standard_dev_all_61_90_summer$sd_2
## z = 2.0339, n = 30, p-value = 0.04196
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## 0.001372517 0.024724573
## sample estimates:
## Sen's slope
## 0.01391241Winter temperature standard deviation
# using the 61-90 data frame
COOP_standard_dev_all_61_90_winter <- COOP_daily_wy_aver_61_90 %>%
filter(waterDay >= 32 & waterDay <= 182) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual))) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_61_90_winter <- COOP_standard_dev_all_61_90_winter %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_61_90_winter %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1961 | 3.915725 |
| 1962 | 6.144011 |
| 1963 | 6.187684 |
| 1964 | 5.258583 |
| 1965 | 5.976798 |
| 1966 | 4.779479 |
| 1967 | 5.036343 |
| 1968 | 4.757923 |
| 1969 | 5.468078 |
| 1970 | 5.116522 |
| 1971 | 5.610921 |
| 1972 | 5.910732 |
| 1973 | 5.367749 |
| 1974 | 4.623904 |
| 1975 | 5.213719 |
| 1976 | 4.574714 |
| 1977 | 4.053940 |
| 1978 | 4.422553 |
| 1979 | 5.671879 |
| 1980 | 5.426113 |
| 1981 | 4.231795 |
| 1982 | 5.115570 |
| 1983 | 4.505216 |
| 1984 | 5.153583 |
| 1985 | 5.080134 |
| 1986 | 5.027172 |
| 1987 | 4.379475 |
| 1988 | 4.862919 |
| 1989 | 5.556552 |
| 1990 | 4.526207 |
ggplot(COOP_standard_dev_all_61_90_winter, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Winter season SD for water years 1961 - 1990
Mann-Kendall & Sen’s Slope
1961 - 1990 winter Mann-Kendall & Sen’s Slope
sd_mk_61_90_winter <- mk.test(COOP_standard_dev_all_61_90_winter$sd_2)
print(sd_mk_61_90_winter)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_61_90_winter$sd_2
## z = -1.8911, n = 30, p-value = 0.0586
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## -107.000000 3141.666667 -0.245977sd_sens_61_90_winter <- sens.slope(COOP_standard_dev_all_61_90_winter$sd_2)
print(sd_sens_61_90_winter)##
## Sen's slope
##
## data: COOP_standard_dev_all_61_90_winter$sd_2
## z = -1.8911, n = 30, p-value = 0.0586
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.052990614 0.002647224
## sample estimates:
## Sen's slope
## -0.024595311991-2020
#average water year temperature
COOP_yearly_wy_aver_91_20 <- coop_steamboat_clean %>%
filter(waterYear >= 1991 & waterYear <= 2020) %>%
group_by(waterYear) %>%
mutate(aver_ann_temp = mean(avg_T_c))
#Average temperature by day for all water years:
COOP_daily_wy_aver_91_20 <- COOP_yearly_wy_aver_91_20 %>%
group_by(daymonth) %>%
mutate(aver_day_temp = mean(avg_T_c))
#average mean temperature by day for the period of record:
COOP_daily_wy_aver_91_20 <- COOP_daily_wy_aver_91_20 %>%
group_by(daymonth) %>%
mutate(all_ave_temp = mean(COOP_daily_wy_aver_91_20$aver_day_temp)) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_91_20 <- COOP_daily_wy_aver_91_20 %>%
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual)))
COOP_standard_dev_91_20 <- COOP_standard_dev_91_20 %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_91_20 %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1991 | 4.145178 |
| 1992 | 3.960420 |
| 1993 | 3.952914 |
| 1994 | 3.687460 |
| 1995 | 3.931778 |
| 1996 | 3.931379 |
| 1997 | 4.023023 |
| 1998 | 3.624635 |
| 1999 | 4.094488 |
| 2000 | 3.636966 |
| 2001 | 4.116507 |
| 2002 | 4.208402 |
| 2003 | 3.707167 |
| 2004 | 3.870138 |
| 2005 | 3.641583 |
| 2006 | 4.115911 |
| 2007 | 4.058783 |
| 2008 | 3.757877 |
| 2009 | 3.718922 |
| 2010 | 3.931119 |
| 2011 | 4.226286 |
| 2012 | 3.267981 |
| 2013 | 4.357008 |
| 2014 | 3.840112 |
| 2015 | 4.062662 |
| 2016 | 3.616594 |
| 2017 | 4.330801 |
| 2018 | 3.629519 |
| 2019 | 3.966571 |
| 2020 | 4.346426 |
ggplot(COOP_standard_dev_91_20, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
SD for water years 1991 - 2020
1991 - 2020 Mann-Kendall & Sen’s Slope
sd_mk_91_20 <- mk.test(COOP_standard_dev_91_20$sd_2)
print(sd_mk_91_20)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_91_20$sd_2
## z = 0.28546, n = 30, p-value = 0.7753
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## 1.700000e+01 3.141667e+03 3.908046e-02sd_sens_91_20 <- sens.slope(COOP_standard_dev_91_20$sd_2)
print(sd_sens_91_20)##
## Sen's slope
##
## data: COOP_standard_dev_91_20$sd_2
## z = 0.28546, n = 30, p-value = 0.7753
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.009547233 0.014518748
## sample estimates:
## Sen's slope
## 0.001987054Summer and Winter 91-20
Summer temperature standard deviation
# using the 1991- 2020 data frame
COOP_standard_dev_all_91_20_summer <- COOP_daily_wy_aver_91_20 %>%
filter(waterDay >= 244 & waterDay <= 335) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual))) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_91_20_summer <- COOP_standard_dev_all_91_20_summer %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_91_20_summer %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1991 | 1.789832 |
| 1992 | 2.359212 |
| 1993 | 2.532099 |
| 1994 | 2.202599 |
| 1995 | 2.726217 |
| 1996 | 1.829452 |
| 1997 | 2.321800 |
| 1998 | 2.308894 |
| 1999 | 1.830620 |
| 2000 | 2.179309 |
| 2001 | 2.606471 |
| 2002 | 2.519656 |
| 2003 | 2.373394 |
| 2004 | 2.524740 |
| 2005 | 2.217524 |
| 2006 | 2.152131 |
| 2007 | 2.361085 |
| 2008 | 2.517364 |
| 2009 | 1.889837 |
| 2010 | 2.127676 |
| 2011 | 1.800363 |
| 2012 | 2.030146 |
| 2013 | 1.961039 |
| 2014 | 2.194757 |
| 2015 | 2.357020 |
| 2016 | 2.075895 |
| 2017 | 1.871864 |
| 2018 | 2.322724 |
| 2019 | 2.363316 |
| 2020 | 2.457244 |
ggplot(COOP_standard_dev_all_91_20_summer, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_line(aes) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Summer season SD for water years 1991 - 2020
Mann-Kendall & Sen’s Slope
Summer 91-20 standard deviations.
sd_mk_91_20_summer <- mk.test(COOP_standard_dev_all_91_20_summer$sd_2)
print(sd_mk_91_20_summer)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_91_20_summer$sd_2
## z = -0.6066, n = 30, p-value = 0.5441
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## -35.00000000 3141.66666667 -0.08045977sd_sens_91_20_summer <- sens.slope(COOP_standard_dev_all_91_20_summer$sd_2)
print(sd_sens_91_20_summer)##
## Sen's slope
##
## data: COOP_standard_dev_all_91_20_summer$sd_2
## z = -0.6066, n = 30, p-value = 0.5441
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.016065617 0.007924092
## sample estimates:
## Sen's slope
## -0.003377995Winter temperature standard deviation
# using the 91-20 data frame
COOP_standard_dev_all_91_20_winter <- COOP_daily_wy_aver_91_20 %>%
filter(waterDay >= 32 & waterDay <= 182) %>% # this might be better off as daymonth rather than day of water year due to leap year
group_by(waterYear) %>%
mutate(residual = (all_ave_temp-aver_ann_temp)+avg_T_c-aver_day_temp) %>%
mutate(deviation = abs(residual-lag(residual))) %>%
group_by(waterYear) %>%
mutate(nmbr = n())
COOP_standard_dev_all_91_20_winter <- COOP_standard_dev_all_91_20_winter %>%
group_by(waterYear) %>%
mutate(resid_mean = mean(residual)) %>%
mutate(sd_1 = residual-resid_mean) %>%
mutate(sd_2 = (((sum((sd_1)^2))/((nmbr-1))))^(0.5)) %>%
distinct(sd_2, .keep_all = TRUE) %>%
select(waterYear, sd_2)
COOP_standard_dev_all_91_20_winter %>%
kable(.,'html') %>%
kable_styling() %>%
scroll_box(width='250px',height='500px')| waterYear | sd_2 |
|---|---|
| 1991 | 5.396681 |
| 1992 | 4.580670 |
| 1993 | 4.981946 |
| 1994 | 4.620652 |
| 1995 | 4.894775 |
| 1996 | 4.826410 |
| 1997 | 4.651361 |
| 1998 | 4.557633 |
| 1999 | 5.099055 |
| 2000 | 4.117501 |
| 2001 | 4.813144 |
| 2002 | 5.120805 |
| 2003 | 4.571242 |
| 2004 | 4.673569 |
| 2005 | 4.517890 |
| 2006 | 5.137815 |
| 2007 | 5.033545 |
| 2008 | 4.361708 |
| 2009 | 4.495860 |
| 2010 | 4.700523 |
| 2011 | 5.632693 |
| 2012 | 3.900892 |
| 2013 | 5.441360 |
| 2014 | 4.616701 |
| 2015 | 5.074498 |
| 2016 | 4.353704 |
| 2017 | 5.348487 |
| 2018 | 4.344638 |
| 2019 | 4.663561 |
| 2020 | 4.607316 |
#This was trying to help Nick Chohan out with a figure issue. Fail!
coef(lm(waterYear ~ sd_2, data= COOP_standard_dev_all_91_20_winter))## (Intercept) sd_2
## 2014.582247 -1.903548#str(COOP_standard_dev_all_91_20_winter)
ggplot(COOP_standard_dev_all_91_20_winter, aes(x = waterYear, y = sd_2))+#, color = waterYear)) +
geom_line(size= 0.7) +
#geom_abline(xintercept = 1990, slope = 0.01333333, size =1) +
theme_few() +
geom_smooth(method = "lm", se=FALSE) +
ylab('SD') +
xlab('Water year')
Winter season SD for water years 1991 - 2020
Mann-Kendall & Sen’s Slope
1991 - 2020 winter Mann-Kendall & Sen’s Slope
sd_mk_91_20_winter <- mk.test(COOP_standard_dev_all_91_20_winter$sd_2)
print(sd_mk_91_20_winter)##
## Mann-Kendall trend test
##
## data: COOP_standard_dev_all_91_20_winter$sd_2
## z = -0.78501, n = 30, p-value = 0.4325
## alternative hypothesis: true S is not equal to 0
## sample estimates:
## S varS tau
## -45.0000000 3141.6666667 -0.1034483sd_sens_91_20_winter <- sens.slope(COOP_standard_dev_all_91_20_winter$sd_2)
print(sd_sens_91_20_winter)##
## Sen's slope
##
## data: COOP_standard_dev_all_91_20_winter$sd_2
## z = -0.78501, n = 30, p-value = 0.4325
## alternative hypothesis: true z is not equal to 0
## 95 percent confidence interval:
## -0.02269601 0.01097902
## sample estimates:
## Sen's slope
## -0.005677511Comparing Climate Normal means
(from 356:)
1991-2022
COOP_yearly_wy_aver_1991_2022_CN <- coop_steamboat_clean %>%
filter(waterYear >= 1991 & waterYear <= 2022) %>%
group_by(waterYear) %>%
mutate(aver_ann_temp = mean(avg_T_c))
#Average temperature by day for all water years:
COOP_daily_wy_aver_1991_2022_CN <- COOP_yearly_wy_aver_1991_2022_CN %>%
group_by(daymonth) %>%
mutate(aver_day_temp = mean(avg_T_c))
#average mean temperature by day for the period of record:
COOP_daily_wy_aver_1991_2022_CN <- COOP_daily_wy_aver_1991_2022_CN %>%
group_by(daymonth) %>%
mutate(all_ave_temp = mean(COOP_daily_wy_aver_1991_2022_CN$aver_day_temp)) %>%
group_by(waterYear) %>%
mutate(nmbr = n())COOP_daily_wy_aver_1991_2022_CN2 <- COOP_daily_wy_aver_1991_2022_CN %>%
#filter(waterYear == "1987" | waterYear == "2021") %>%
group_by(waterDay) %>%
mutate(date_temp = mean(avg_T_c))%>%
select(waterDay, date_temp) %>%
distinct(waterDay, .keep_all = TRUE)
COOP_daily_wy_aver_1991_2022_CN2$date_temp <- signif(COOP_daily_wy_aver_1991_2022_CN2$date_temp,3) #reduce the sig figs
ggplot(COOP_daily_wy_aver_1991_2022_CN2, aes(x = waterDay, y = date_temp))+#, color = waterYear)) +
geom_line(size= 0.7) +
theme_few()
1961-1990
COOP_yearly_wy_aver_1961_1990_CN <- coop_steamboat_clean %>%
filter(waterYear >= 1961 & waterYear <= 1990) %>%
group_by(waterYear) %>%
mutate(aver_ann_temp = mean(avg_T_c))
#Average temperature by day for all water years:
COOP_daily_wy_aver_1961_1990_CN <- COOP_yearly_wy_aver_1961_1990_CN %>%
group_by(daymonth) %>%
mutate(aver_day_temp = mean(avg_T_c))
#average mean temperature by day for the period of record:
COOP_daily_wy_aver_1961_1990_CN <- COOP_daily_wy_aver_1961_1990_CN %>%
group_by(daymonth) %>%
mutate(all_ave_temp = mean(COOP_daily_wy_aver_1961_1990_CN$aver_day_temp)) %>%
group_by(waterYear) %>%
mutate(nmbr = n())COOP_daily_wy_aver_1961_1990_CN2 <- COOP_daily_wy_aver_1961_1990_CN %>%
#filter(waterYear == "1987" | waterYear == "2021") %>%
group_by(waterDay) %>%
mutate(date_temp = mean(avg_T_c))%>%
select(waterDay, date_temp) %>%
distinct(waterDay, .keep_all = TRUE)
COOP_daily_wy_aver_1961_1990_CN2$date_temp <- signif(COOP_daily_wy_aver_1961_1990_CN2$date_temp,3) #reduce the sig figs
ggplot(COOP_daily_wy_aver_1961_1990_CN2, aes(x = waterDay, y = date_temp))+#, color = waterYear)) +
geom_line(size= 0.7) +
theme_few()
1931-1960
COOP_yearly_wy_aver_1931_1960_CN <- coop_steamboat_clean %>%
filter(waterYear >= 1931 & waterYear <= 1960) %>%
group_by(waterYear) %>%
mutate(aver_ann_temp = mean(avg_T_c))
#Average temperature by day for all water years:
COOP_daily_wy_aver_1931_1960_CN <- COOP_yearly_wy_aver_1931_1960_CN %>%
group_by(daymonth) %>%
mutate(aver_day_temp = mean(avg_T_c))
#average mean temperature by day for the period of record:
COOP_daily_wy_aver_1931_1960_CN <- COOP_daily_wy_aver_1931_1960_CN %>%
group_by(daymonth) %>%
mutate(all_ave_temp = mean(COOP_daily_wy_aver_1931_1960_CN$aver_day_temp)) %>%
group_by(waterYear) %>%
mutate(nmbr = n())COOP_daily_wy_aver_1931_1960_CN2 <- COOP_daily_wy_aver_1931_1960_CN %>%
#filter(waterYear == "1987" | waterYear == "2021") %>%
group_by(waterDay) %>%
mutate(date_temp = mean(avg_T_c))%>%
select(waterDay, date_temp) %>%
distinct(waterDay, .keep_all = TRUE)
COOP_daily_wy_aver_1931_1960_CN2$date_temp <- signif(COOP_daily_wy_aver_1931_1960_CN2$date_temp,3) #reduce the sig figs
ggplot(COOP_daily_wy_aver_1931_1960_CN2, aes(x = waterDay, y = date_temp))+#, color = waterYear)) +
geom_line(size= 0.7) +
theme_few()
1900-1930
COOP_yearly_wy_aver_1901_1930_CN <- coop_steamboat_clean %>%
filter(waterYear >= 1901 & waterYear <= 1930) %>%
group_by(waterYear) %>%
mutate(aver_ann_temp = mean(avg_T_c))
#Average temperature by day for all water years:
COOP_daily_wy_aver_1901_1930_CN <- COOP_yearly_wy_aver_1901_1930_CN %>%
group_by(daymonth) %>%
mutate(aver_day_temp = mean(avg_T_c))
#average mean temperature by day for the period of record:
COOP_daily_wy_aver_1901_1930_CN <- COOP_daily_wy_aver_1901_1930_CN %>%
group_by(daymonth) %>%
mutate(all_ave_temp = mean(COOP_daily_wy_aver_1901_1930_CN$aver_day_temp)) %>%
group_by(waterYear) %>%
mutate(nmbr = n())COOP_daily_wy_aver_1901_1930_CN2 <- COOP_daily_wy_aver_1901_1930_CN %>%
#filter(waterYear == "1987" | waterYear == "2021") %>%
group_by(waterDay) %>%
mutate(date_temp = mean(avg_T_c))%>%
select(waterDay, date_temp) %>%
distinct(waterDay, .keep_all = TRUE)
COOP_daily_wy_aver_1901_1930_CN2$date_temp <- signif(COOP_daily_wy_aver_1901_1930_CN2$date_temp,3) #reduce the sig figs
ggplot(COOP_daily_wy_aver_1901_1930_CN2, aes(x = waterDay, y = date_temp))+#, color = waterYear)) +
geom_line(size= 0.7) +
theme_few()
climate_norm <- left_join(COOP_daily_wy_aver_1991_2022_CN2, COOP_daily_wy_aver_1961_1990_CN2, by= 'waterDay')
climate_norm <- left_join(climate_norm, COOP_daily_wy_aver_1931_1960_CN2, by= 'waterDay')
climate_norm <- left_join(climate_norm, COOP_daily_wy_aver_1901_1930_CN2, by= 'waterDay')
ggplot(climate_norm, aes(x=waterDay)) +
#scale_size_manual(values=c("FALSE"=5,"TRUE"=8)) +
#scale_color_manual(values=c("FALSE"='#CC0000',"TRUE"='black')) +
geom_line(aes(y = date_temp.x, color = "1991-2022"), size =1)+
geom_line(aes(y = date_temp.y, color = "1961-1990"), size =1)+
geom_line(aes(y = date_temp.x.x, color = "1931-1960"), size =1)+
geom_line(aes(y = date_temp.y.y, color= "1901-1930"), size =1)+
theme_few()+
scale_colour_manual(name = "climate normals", values=c("1991-2022" = "orange", "1961-1990" = "darkgreen", "1931-1960" = "purple", "1901-1930" = "red"))+
xlab("Day of Year")+
ylab("Average temperature")